{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model: Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import _pickle as pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('../top10_features.xlsx')\n",
    "df = df.drop(df.columns[0], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "features_df = df.drop([\"Decision\"], 1)\n",
    "\n",
    "scaled_df = pd.DataFrame(scaler.fit_transform(features_df), \n",
    "                               index=features_df.index, \n",
    "                               columns=features_df.columns)\n",
    "\n",
    "df = scaled_df.join(df.Decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"Decision\"], 1)\n",
    "y = df.Decision\n",
    "\n",
    "# Train, test, split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function for plotting confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred, labels=[\"Sell\", \"Buy\", \"Hold\"], \n",
    "                          normalize=False, title=None, cmap=plt.cm.coolwarm):\n",
    "\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    fig, ax = plt.subplots(figsize=(12,6))\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    # We want to show all ticks...\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=labels, yticklabels=labels,\n",
    "           title=title,\n",
    "           ylabel='ACTUAL',\n",
    "           xlabel='PREDICTED')\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 1.5\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"snow\" if cm[i, j] > thresh else \"orange\",\n",
    "                    size=26)\n",
    "    ax.grid(False)\n",
    "    fig.tight_layout()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "The preferred evaluation metric used will be __Precision__ for each class.  They will be optimized using the __F1 Score-Macro-Average__ to balance the Precision and Recall.  This is done because we want to not only be correct when predicting but also make a decent amount of predictions for each class.  Classes such as 'Buy' and 'Sell' are more important than 'Hold'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Fitting and training\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing out Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.80      0.44      0.57         9\n",
      "         Buy       0.50      0.86      0.63         7\n",
      "        Hold       0.00      0.00      0.00         1\n",
      "\n",
      "    accuracy                           0.59        17\n",
      "   macro avg       0.43      0.43      0.40        17\n",
      "weighted avg       0.63      0.59      0.56        17\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "pred = clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters to Tune\n",
    "params = {'penalty': ['l1', 'l2'],\n",
    "          'C': [1.5**n for n in range(0, 20, 5)],\n",
    "          'fit_intercept': [True, False],\n",
    "          'intercept_scaling': [1, 10, 50, 100],\n",
    "          'solver': ['liblinear', 'saga']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 128 candidates, totalling 384 fits\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.511, test=0.327), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.462, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.450, test=0.253), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.491, test=0.293), total=   0.1s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.463, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.432, test=0.253), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.510, test=0.293), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.462, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.523, test=0.263), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.510, test=0.293), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.462, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.523, test=0.299), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.511, test=0.327), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.478, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.450, test=0.253), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.491, test=0.293), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.463, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.432, test=0.253), total=   0.1s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.510, test=0.293), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.462, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.523, test=0.299), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.510, test=0.293), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.462, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.523, test=0.299), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.511, test=0.327), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.478, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.450, test=0.253), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.491, test=0.293), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.463, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.432, test=0.253), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.510, test=0.293), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.462, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.523, test=0.299), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.510, test=0.293), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.462, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.523, test=0.299), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.511, test=0.327), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.478, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.450, test=0.253), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.491, test=0.293), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.463, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.432, test=0.308), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.510, test=0.293), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.462, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.523, test=0.299), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.510, test=0.293), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.462, test=0.292), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.523, test=0.299), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.484, test=0.411), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.493, test=0.308), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.583, test=0.258), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.477, test=0.263), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.545, test=0.376), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.560, test=0.258), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.472, test=0.228), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.525, test=0.412), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.514, test=0.233), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.472, test=0.228), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.546, test=0.412), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.514, test=0.233), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.484, test=0.411), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.493, test=0.308), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.583, test=0.258), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.477, test=0.263), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.545, test=0.376), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.560, test=0.258), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.472, test=0.228), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.525, test=0.412), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.514, test=0.233), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.472, test=0.228), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.546, test=0.412), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.514, test=0.233), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.484, test=0.411), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.493, test=0.308), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.583, test=0.258), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.477, test=0.263), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.545, test=0.376), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.560, test=0.258), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.472, test=0.228), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.525, test=0.412), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.514, test=0.233), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.472, test=0.228), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.546, test=0.412), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.514, test=0.233), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.484, test=0.411), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.493, test=0.308), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.583, test=0.258), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.477, test=0.263), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.545, test=0.376), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.560, test=0.258), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.472, test=0.228), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.525, test=0.412), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.514, test=0.233), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.472, test=0.228), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.546, test=0.412), total=   0.0s\n",
      "[CV] C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=1.0, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.514, test=0.233), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.525, test=0.334), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.494, test=0.284), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.555, test=0.225), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.462, test=0.292), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.522, test=0.222), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.502, test=0.334), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.555, test=0.225), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.522, test=0.222), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.525, test=0.307), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.494, test=0.292), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.555, test=0.225), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.462, test=0.292), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.522, test=0.222), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.502, test=0.334), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.467, test=0.284), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.555, test=0.225), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.510, test=0.293), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.522, test=0.222), total=   0.1s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.525, test=0.307), total=   0.1s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.494, test=0.292), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.555, test=0.225), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.462, test=0.292), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.522, test=0.222), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.502, test=0.334), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.467, test=0.284), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.555, test=0.225), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.522, test=0.222), total=   0.1s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.525, test=0.307), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.494, test=0.292), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.555, test=0.225), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.462, test=0.292), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.522, test=0.222), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.502, test=0.334), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.467, test=0.284), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.555, test=0.225), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.467, test=0.292), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.522, test=0.222), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.493, test=0.390), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.472, test=0.308), total=   0.1s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.559, test=0.233), total=   0.1s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.525, test=0.377), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.471, test=0.256), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.492, test=0.413), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.557, test=0.233), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.471, test=0.228), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.493, test=0.390), total=   0.1s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.472, test=0.308), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.559, test=0.233), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.525, test=0.377), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.471, test=0.256), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.492, test=0.413), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.557, test=0.233), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.493, test=0.390), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.472, test=0.308), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.559, test=0.233), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.471, test=0.228), total=   0.1s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.525, test=0.377), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.471, test=0.256), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.492, test=0.413), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.557, test=0.233), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.579, test=0.233), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.493, test=0.390), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.472, test=0.308), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.559, test=0.233), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.525, test=0.377), total=   0.1s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.579, test=0.233), total=   0.1s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.471, test=0.256), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.492, test=0.413), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.557, test=0.233), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.471, test=0.228), total=   0.1s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=7.59375, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.616, test=0.278), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.462, test=0.285), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.683, test=0.203), total=   0.1s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.522, test=0.225), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.598, test=0.278), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.477, test=0.256), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.555, test=0.225), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.510, test=0.327), total=   0.1s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.467, test=0.292), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.522, test=0.225), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.632, test=0.278), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.462, test=0.285), total=   0.1s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.683, test=0.203), total=   0.1s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.522, test=0.225), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.598, test=0.278), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.477, test=0.256), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.555, test=0.225), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.522, test=0.225), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.616, test=0.278), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.462, test=0.285), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.683, test=0.203), total=   0.1s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.522, test=0.225), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.598, test=0.278), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.477, test=0.256), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.555, test=0.225), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.522, test=0.225), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.632, test=0.278), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.462, test=0.285), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.683, test=0.203), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.522, test=0.225), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.598, test=0.278), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.477, test=0.256), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.555, test=0.225), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.522, test=0.225), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.463, test=0.390), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.549, test=0.459), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.520, test=0.233), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.478, test=0.390), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.530, test=0.382), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.541, test=0.233), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.463, test=0.390), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.549, test=0.459), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.520, test=0.233), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.579, test=0.233), total=   0.1s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.478, test=0.390), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.530, test=0.382), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.541, test=0.233), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.463, test=0.390), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.549, test=0.459), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.520, test=0.233), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.557, test=0.233), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.478, test=0.390), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.530, test=0.382), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.541, test=0.233), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.463, test=0.390), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.549, test=0.459), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.520, test=0.233), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.579, test=0.233), total=   0.1s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.478, test=0.390), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.530, test=0.382), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.541, test=0.233), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=57.6650390625, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.632, test=0.278), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.547, test=0.285), total=   0.1s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.683, test=0.203), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.522, test=0.225), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.632, test=0.278), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.564, test=0.284), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.667, test=0.203), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.522, test=0.225), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.632, test=0.278), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.547, test=0.285), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.667, test=0.203), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.522, test=0.225), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.632, test=0.278), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.564, test=0.284), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.667, test=0.203), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.467, test=0.292), total=   0.1s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.522, test=0.225), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.632, test=0.278), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.547, test=0.285), total=   0.1s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.667, test=0.203), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.522, test=0.225), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.632, test=0.278), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.564, test=0.256), total=   0.1s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.667, test=0.203), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.522, test=0.225), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.632, test=0.278), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.547, test=0.285), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.667, test=0.203), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.522, test=0.225), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.632, test=0.278), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.564, test=0.256), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.667, test=0.203), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.510, test=0.327), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.467, test=0.292), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=True, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.522, test=0.225), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.439, test=0.390), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.549, test=0.459), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=liblinear, score=(train=0.541, test=0.233), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l1, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.439, test=0.390), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.567, test=0.459), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=liblinear, score=(train=0.541, test=0.233), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=1, penalty=l2, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.439, test=0.390), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.549, test=0.459), total=   0.1s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=liblinear, score=(train=0.541, test=0.233), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l1, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.439, test=0.390), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.567, test=0.459), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=liblinear, score=(train=0.541, test=0.233), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=10, penalty=l2, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.439, test=0.390), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.549, test=0.459), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=liblinear, score=(train=0.541, test=0.233), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l1, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.439, test=0.390), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.567, test=0.459), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=liblinear, score=(train=0.541, test=0.233), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=50, penalty=l2, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.439, test=0.390), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.549, test=0.459), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=liblinear, score=(train=0.541, test=0.233), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l1, solver=saga, score=(train=0.579, test=0.233), total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.439, test=0.390), total=   0.1s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.567, test=0.459), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=liblinear, score=(train=0.541, test=0.233), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.471, test=0.228), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.524, test=0.377), total=   0.0s\n",
      "[CV] C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga \n",
      "[CV]  C=437.8938903808594, fit_intercept=False, intercept_scaling=100, penalty=l2, solver=saga, score=(train=0.579, test=0.233), total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
      "[Parallel(n_jobs=1)]: Done 384 out of 384 | elapsed:   11.6s finished\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                          fit_intercept=True,\n",
       "                                          intercept_scaling=1, l1_ratio=None,\n",
       "                                          max_iter=100, multi_class='warn',\n",
       "                                          n_jobs=None, penalty='l2',\n",
       "                                          random_state=None, solver='warn',\n",
       "                                          tol=0.0001, verbose=0,\n",
       "                                          warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'C': [1.0, 7.59375, 57.6650390625, 437.8938903808594],\n",
       "                         'fit_intercept': [True, False],\n",
       "                         'intercept_scaling': [1, 10, 50, 100],\n",
       "                         'penalty': ['l1', 'l2'],\n",
       "                         'solver': ['liblinear', 'saga']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='f1_macro', verbose=5)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search = GridSearchCV(clf, params, cv=3, return_train_score=True, verbose=5, scoring='f1_macro')\n",
    "\n",
    "search.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuned Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Training Score: 0.5190979252173615\n",
      "Mean Testing Score: 0.5544365293812604\n",
      "\n",
      "Best Parameter Found:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'C': 57.6650390625,\n",
       " 'fit_intercept': False,\n",
       " 'intercept_scaling': 1,\n",
       " 'penalty': 'l1',\n",
       " 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Mean Training Score:\", np.mean(search.cv_results_['mean_train_score']))\n",
    "print(\"Mean Testing Score:\", search.score(X, y))\n",
    "print(\"\\nBest Parameter Found:\")\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model with the Best Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\72445\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=57.6650390625, class_weight=None, dual=False,\n",
       "                   fit_intercept=False, intercept_scaling=1, l1_ratio=None,\n",
       "                   max_iter=100, multi_class='warn', n_jobs=None, penalty='l1',\n",
       "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_clf = search.best_estimator_\n",
    "\n",
    "search_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results from Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Sell       0.67      0.44      0.53         9\n",
      "         Buy       0.67      0.57      0.62         7\n",
      "        Hold       0.20      1.00      0.33         1\n",
      "\n",
      "    accuracy                           0.53        17\n",
      "   macro avg       0.51      0.67      0.49        17\n",
      "weighted avg       0.64      0.53      0.56        17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classifier predictions\n",
    "s_pred = search_clf.predict(X_test)\n",
    "\n",
    "#Printing out results\n",
    "report = classification_report(y_test, s_pred, target_names=['Sell', 'Buy', 'Hold'])\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix for Optimum Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, s_pred, title=\"Confusion Matrix\")\n",
    "np.set_printoptions(precision=1)\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
